Artificial Intelligence as a Service (AI-aaS) on Software-Defined Infrastructure

Abstract

This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.Index Terms-Artificial intelligence as a service (AI-aaS), cognitive network management, MAPE-K loop, kubernetes, Kubeflow, MEC, micro service management.

Lifting the curtain: Strategic visibility of human labour in AI-as-a-Service

“…Drawing on the technical heritage of Software-as-a-Service, Platform-as-a-Service and Infrastructure-as-a-Service, many organisations are adopting AIaaS, accessing specific AI capabilities through the cloud (Casati et al., 2019; Parsaeefard et al., 2019; Tubaro et al., 2020). In the same way that cloud technologies have democratised access to software, data storage and data processing (Mosco, 2015), AIaaS empowers organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms.…”

“…In the current zeitgeist of research into artificial intelligence (AI), academic interest has largely focused on AI as an abstract sociotechnical phenomenon, or as embedded in major platforms such as Facebook, Google or Twitter. However, this focus has overlooked the widespread growth of commercial AI-as-a-Service offerings (AIaaS), where organisations access specific AI capabilities via cloud computing (Casati et al., 2019; Parsaeefard et al., 2019; Tubaro et al., 2020). Enabling organisations to scale their AI usage based on strategic requirements, AIaaS can range from conversational bots to knowledge mapping, computer vision and speech recognition.…”

Drivers and Inhibitors for Organizations’ Intention to Adopt Artificial Intelligence as a Service

“…Quadrant B shows that researchers have also started to develop and evaluate the technical capabilities of AIaaS. For example, related studies deployed and tested prototypes of AIaaS in the context of software-defined infrastructures and showed new business opportunities for networks by using AIaaS [11].…”

“…Whereas AIaaS comes with unique and innovative features, such as complexity abstraction and pretrained and customizable AI models, it also introduces new challenges, such as opaque data processing and privacy infringement risks. Extant research on AIaaS mostly takes a technical perspective and investigates, for example, how to design AIaaS systems to overcome such challenges [10,11], or how to detect security flaws in AIaaS systems [12]. However, while market research indicates that organizations are quick to adopt AIaaS (e.g., it is expected that the AIaaS market will grow by more than 42 % in 2020 [13]), research provides inconsistent rationales for AIaaS adoption.…”

Monitoring Misuse for Accountable 'Artificial Intelligence as a Service'

“…We increasingly see providers offering 'AI as a Service' (AIaaS). In essence, AIaaS aims at providing the ML (machine learning)-driven building blocks to support customer applications [26]. It includes service providers offering access to a range of pre-built models and related services, whereby customers can send to the service particular inputs and receive back the results of a ML process, e.g.…”